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Wireless sensor networks (WSNs)-based internet of things (IoT) are among the fast booming technologies that drastically contribute to different systems management and resilience data accessibility. Designing a robust IoT network imposes…
Over the last decade, IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services. Because of easy accessibility and fast-growing demand for…
The rapid progress in technology innovation usage and distribution has increased in the last decade. The rapid growth of the Internet of Things (IoT) systems worldwide has increased network security challenges created by malicious third…
Maintaining security in IoT systems depends on intrusion detection since these networks' sensitivity to cyber-attacks is growing. Based on the IoT23 dataset, this study explores the use of several Machine Learning (ML) and Deep Learning…
This paper proposes a novel federated learning approach for improving IoT network intrusion detection. The rise of IoT has expanded the cyber attack surface, making traditional centralized machine learning methods insufficient due to…
The advantages of IoT in strengthening commercial, industrial, and social ecosystems have led to its widespread expansion. Nevertheless, because endpoint devices have limited computation, storage, and communication capabilities, the IoT…
With the rapid development of low-cost consumer electronics and cloud computing, Internet-of-Things (IoT) devices are widely adopted for supporting next-generation distributed systems such as smart cities and industrial control systems. IoT…
This paper studies the effect of various hyper-parameters and their selection for the best performance of the deep learning model proposed in [1] for distributed attack detection in the Internet of Things (IoT). The findings show that there…
The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes…
The generalization of deep learning has helped us, in the past, address challenges such as malware identification and anomaly detection in the network security domain. However, as effective as it is, scarcity of memory and processing power…
The rise of heterogeneous Internet of Things (IoT) devices has raised security concerns due to their vulnerability to cyberattacks. Intrusion Detection Systems (IDS) are crucial in addressing these threats. Federated Learning (FL) offers a…
The current amount of IoT devices and their limitations has come to serve as a motivation for malicious entities to take advantage of such devices and use them for their own gain. To protect against cyberattacks in IoT devices, Machine…
This paper presents a security paradigm for edge devices to defend against various internal and external threats. The first section of the manuscript proposes employing machine learning models to identify MQTT-based (Message Queue Telemetry…
Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a considerable vulnerability and threat to…
In the evolving landscape of the Internet of Things (IoT), Machine Learning (ML)-based Intrusion Detection Systems (IDS) represent a significant advancement, especially when integrated with Software-Defined Networking (SDN). These systems…
The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such…
The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks, making effective detection essential for securing IoT networks. This work introduces a novel approach combining Self-Organizing Maps (SOMs),…
Intrusion Detection Systems (IDSs) are a key component for protecting Internet of Things (IoT) environments. However, in Machine Learning-based (ML-based) IDSs, performance is often degraded by the strong class imbalance between benign and…
In the present era of advanced technology, the Internet of Things (IoT) plays a crucial role in enabling smart connected environments. This includes various domains such as smart homes, smart healthcare, smart cities, smart vehicles, and…
A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network interruptions and loss of sensitive data have occurred, which led…